Analytically determining revenue of internet companies using internet metrics

ABSTRACT

With respect to a current quarter of unreported revenue for certain Internet companies, by processes performed by a computer revenue to date is analytically determined and future revenue for the remaining quarter is statistically projected by modeling revenue based on “Internet metrics”. Actual revenue performance is obtained and one or more “Internet metrics” are measured for a given Internet company. Using the revenue and measured Internet metric data from prior quarters, a regression analysis is performed in order to generate multiple models that reflect the relationship between the Internet metrics and revenue. From these models, one is selected that will most likely yield the best revenue estimates. This resultant model and current Internet metric data are subsequently used to estimate the company&#39;s revenue for the current day, week, month, or quarter. These estimates are also used to project the company&#39;s revenue for future days, weeks, months, and quarters.

RELATED APPLICATION

[0001] The present application claims the benefit of U.S. ProvisionalApplication No. 60/288,769 filed on May 4, 2001, entitled “Methods forAnalytically Determining Revenue of Internet Companies Using InternetMetrics.”

BACKGROUND OF OUR INVENTION

[0002] 1. Field of the Invention

[0003] Our invention relates to methods for analytically determining therevenue of certain types of Internet companies. More particularly, ourinvention relates to methods for using Web based and equipment basedmetrics related to Internet companies for analytically determining thecurrent revenue and statistically projecting the future revenue of thesecompanies.

[0004] 2. Description of the Background

[0005] People are continuing to use the Internet as a medium forcommunication, education, entertainment, information exchange,electronic commerce (E-commerce), etc. Accordingly, new businesses areemerging and businesses in virtually every sector of the economy areusing the Internet to provide new services and reach new and existingcustomers more effectively and cheaply. In particular, this inventionrelates to firms including pure E-commerce companies, “click and mortar”companies, portals, and Internet Service Providers (ISPs). Hereinafter,these types of companies will be collectively referred to as “Internet”companies. Although there are many other types of companies whosebusiness relates to the Internet, our focus is on the types of Internetcompanies just listed.

[0006] The financial community typically does not become aware of therevenues generated by “traditional” companies until several weeks afterthe company quarters end, when revenue data is announced. The same holdstrue for the above “Internet” companies. Although past quarterly data isuseful, the financial community needs daily, weekly, and monthlyinformation, as well as projections to the end of the quarter, to aid intheir everyday decision-making. As such, there is a need by thefinancial community to estimate and forecast the revenue performance ofthe Internet sector. In addition to using information directly providedby companies, financial institutions currently use fundamental andtechnical analysis, such as revenue estimates based on number ofemployees, past sales analysis, and trend analysis of past revenues, toestimate and forecast revenue. However, given both the rate at which theInternet in general is growing and the volatility within the Internetsector, these estimation and forecast techniques are proving to beinadequate. In addition, there is always a need to make more accurateestimates on a more timely basis.

SUMMARY OF OUR INVENTION

[0007] It is desirable to provide methods that overcome the shortcomingsof the prior art and more accurately estimate and project, on a moretimely basis, the economic performance of an Internet company. Ourinvention satisfies these and other desires by providing a methodperformed by a computer for estimating current revenue and projectingfuture revenue of an Internet company through Web based and equipmentbased metrics related to that company.

[0008] Through experimentation and research, we have discovered thatcertain physical events that occur at an Internet company's Webenvironment and the amount of certain types of physical equipment usedby an Internet company are strongly correlated to and predictive of therevenue generated by that company. We refer to measures of thesephysical events and physical equipment as “Internet metrics”. Based onour discovery, we have invented methods for estimating current revenueand projecting future revenue of an Internet company, thereby overcomingthe issues of the prior art. Specifically, we have discovered that atleast four Internet metrics are highly correlated to the revenuegenerated by Internet companies and when properly modeled, these metricscan be used to estimate company revenue for the current day, week,month, and quarter, and to project company revenue for future days,weeks, months, and quarters.

[0009] The Internet metrics we determined to be predictive of revenueinclude: the number of page hits at a company's Web site (“page-hitsmetric”), the number of visitors to a company's Web site (“visitorsmetric”), the number of transactions conducted at a company's Web site(“transactions metric”), and the number of Internet hosts (i.e., IPaddresses) supported by an Internet Service Provider (“hosts metric”). Afifth metric, currently under study to verify its correlative nature, isthe “delay” within an Internet company's web environment (“delaymetric”), which is a measure of how busy the servers, routers, and otherequipment are. Each of these metrics represents a numerical countrelative to the duration of time over which the metric is measured. Assuch, “page-hits” represents the sum, over all visitors, of the numberof pages browsed by each visitor at an Internet company's Web site overeach measurement period.

[0010] “Visitors” represents the number of “unique” visitors to visit anInternet company's Web site over each measurement period. “Transactions”represents the number of physical transactions to occur at an Internetcompany's Web site over each measurement period. “Transactions” is basedon “https requests” and is currently measured by counting allhttps-requests that begin with “https://”. However, transaction countscan also be determined by counting sub-fields of the “https://”requests. “Hosts” represents the number of IP addresses supported by anISP over each measurement period. Although we have discovered that thesemetrics are strong indicators of revenue, nothing in our inventionprecludes the use of other Internet metrics to estimate revenue as thesemetrics may arise as the Internet industry continues to develop.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011]FIG. 1 is a flow chart of a method for determining a revenue modelfor an Internet company in accordance with the present invention.

[0012]FIG. 2 is a high level block diagram of a computer, Internetcompany Web site, and processors for collecting Web based and equipmentbased metrics, on which computer can be implemented methods forestimating current revenue and for projecting future revenue of theInternet company based on the collected Web and equipment based metricsin accordance with the present invention.

[0013]FIG. 3 is a chart illustrating weekly revenue estimates for anInternet company made using methods in accordance with the presentinvention.

DETAILED DESCRIPTION

[0014] Our inventive method for estimating and projecting revenuecomprises three general steps, as shown by FIG. 1. In the first step,102, actual quarterly revenue performance is obtained and one or more ofthe “Internet metrics” are measured for a given Internet company and aremaintained within an on-going computer database. In some instances, onlyone metric is most relevant to a given company and, in other instances,multiple metrics are most relevant. In the second step, 104, the revenueand measured Internet metric data from prior quarters are used toperform regression analyses in order to generate multiple models thatreflect the relationship between the Internet metrics and revenue. Fromthese models, one is selected that will most likely yield the bestrevenue estimates. In the last step, 106, the resultant model andcurrent Internet metric data are used to estimate the revenue for thecurrent day, week, month, and quarter, and to project the revenue forfuture days, weeks, months, and quarters. Each of these steps is furtherdescribed below. The methods in accordance with embodiments of ourinvention are executed by a computer. For example, as illustrated inFIG. 2, software control for the method steps in accordance with thepresent invention may be stored as software in memory 204 and executedon processor 205 within computer 202.

[0015] The first general step of our invention, step 102, requires theon-going collection of data points, these data points constitutingactual quarterly revenue performance and the Internet metrics. Therevenue data is readily available, as it is publicly released followingthe end of a quarter. The Internet metrics are not as easily obtainedalthough various methods exist; however, no one method is critical toour invention. In general, data points relative to the page-hits,transactions, and visitors metrics are obtained by examining the Webactivity related to consumers browsing an Internet company's Web site.Data points relative to the hosts metric and delay metric are moredifficult to obtain. Several Internet collection methods are brieflydescribed below, which methods can be categorized as direct andindirect. Regardless of the method of collection used, the results areultimately stored in a database, such as a database 206 in computer 202,and are required for steps 104 and 106 of our invention, as describedbelow.

[0016] A first collection method is to obtain the data directly from anInternet company. For example, Web servers typically log accessactivity. These logs can be used to determine data points for thetransactions, visitors, and page-hits metrics. Hosts counts can beobtained directly from an ISP's management systems. This data cansubsequently be uploaded to computer 202.

[0017] A second collection method is to indirectly obtain the metrics,without the help of an Internet company, as seen in FIG. 2. One suchmethod is to collect the page-hits, transactions, and visitors metricsthrough random sampling. Under this method, a population set is chosenand each member of this set agrees to have his/her personal computer logall Internet activity relative to particular Internet companies. Theselogs are then analyzed and the results statistically adjusted torepresent the public in general. Several companies currently providesuch services. A second method to gather the page-hits, transactions,and visitors metrics is to physically monitor a network and gather thedata, discarding user specific data. With respect to the delay metric,one method is to transmit test packets to a companies web server(s) andmeasure the response time. Sophisticated algorithms are applied to thisresponse time to eliminate time spent within the public Internet networkand to estimate how “busy” the equipment (routers, servers, etc.) withinthe Web environment is. With respect to the hosts metric, U.S. Pat. No.6,178,451 B 1, “Computer Network Size Growth Forecasting Method andSystem”, by C. Huitema and S. Weerahandi, describes a method forobtaining an ISP's hosts counts, the teachings of which are incorporatedherein by reference. Nothing in our invention precludes using othermethods for collecting data.

[0018] Once collected, the revenue and Internet metric data points arecategorized into three general categories: (1) past actual revenueperformance, (2) past Internet metrics, and (3) current Internetmetrics. With respect to the terms “past” and “current”, “past” data isall data collected up through the most recently reported quarterlyrevenue and “current” data is all data collected since the most recentlyreported quarterly revenue. In accordance with the methods of ourinvention, the past revenue and past Internet metric data are used togenerate a revenue model (step 104) that is subsequently applied to thecurrent Internet metrics to estimate and project revenue (step 106).

[0019] Because data collection is on going, at the close of a quarter,the current data becomes past data and is subsequently used to generatea model for the next quarter. However, our research has shown that dueto the volatility and rate at which the Internet industry is changing,“past” data becomes less predictive of revenue the “older” the past databecomes. As such, in accordance with the methods of our invention, nomore than six quarters of past revenue and Internet metric data are usedto generate the next quarter's model. From a pictorial standpoint, a“moving-window” is placed over the data and advanced by one quarter atthe end of each quarter. However, as the Internet industry stabilizes,nothing in our invention precludes the widening or narrowing of thiswindow to include more or less past data in generating models

[0020] As indicated, past revenue is collected on a quarterly basis dueto the methods of reporting. For the purposes of discussion, thesequarterly data points can be expressed as a data set as shown inequation (1), where “m” represents the most recently reported quarterlyrevenue and “n” represents the number of quarters over which theregression analysis will be performed (as indicated, n is currently setto 6).

{R}={ . . . , R _((m−n)) , . . . , R _((m−2)) , R _((m−1)) , R_(m)}  (1)

[0021] With respect to the Internet metrics, each metric represents acount of a physical event or physical device. Currently, the collectionmethods used by our invention measure these events and devices on aweekly basis, although daily, monthly, or quarterly counts can also bemade depending on the method of collection. Not all metrics apply to allcompanies and therefore not all counts are performed for all companies.Assuming, for discussion purposes, that all five metrics described aboveare collected for a given company on a weekly basis, the set of weeklymetric data points for each metric can be expressed as equations (2-6)

{D _(trans) }={ . . . , D _((trans)(k−j)) , . . . , D _((trans)(k−2)) ,D _((trans)(k−1)) , D _((trans)k) , D _((trans)(k+1)) , D_((trans)(k+2)), . . . }  (2)

{D _(page-hits) }={ . . . , D _((page-hits)(k−j)) , . . . , D_((page-hits)(k−2)) , D _((page-hits)(k−1)) , D _((page-hits)k) , D_((page-hits)(k+1)) , D _((page-hits)(k+2)), . . . }  (3)

{D _(visitors) }={D _((visitors)(k−j)) , . . . , D _((visitors)(k−2)) ,D _((visitors)(k−1)) , D _((visitors)k) , D _((visitors)(k+1)) ,D(visitors)(k+2), . . . }  (4)

{D _(host) }={ . . . , D _((hosts)(k−j)) , . . . , D _((hosts)(k−2)) , D_((hosts)(k−1)) , D _((hosts)k) , D _((hosts)(k+1)) , D _((hosts)(k+2)),. . . }  (5)

{D _(delay) }={ . . . , D _((delay)(k−j)) , . . . , D _((delay)(k−2)) ,D _((delay)(k−1)) , D _((delay)k) , D _((delay)(k+1)) , D_((delay)(k+2)), . . . }  (6)

[0022] where the k^(th) data point is the last weekly measurement madefor the last quarter, the (k−j)^(th) data point is the oldest past datapoint that will be used to determine the current model, and the(k+1)^(th), (k+2)^(th), etc. data points are weekly measurements for thecurrent quarter.

[0023] In accordance with the methods of our invention, determination ofthe revenue models in step 104 below requires that the data pointscomprising the past Internet metrics be expressed on the same scale asthe revenue data. As a result, assuming again that all five metrics arecollected for a given company on a weekly basis, the (k−j)^(th) tok^(th) data points in equations (2)-(6) must be combined and scaled to“quarterly” counts prior to beginning step 104. The result is a new setof “Past” “quarterly” metric data points and can be expressed as shownin equations (7)-(11), where “m” represents the quarterly data pointcorresponding to the most recently reported quarterly revenue and “n”represents the number of quarters over which the regression analysiswill be performed.

{P _(trans) }={ . . . , P _((trans)(m−n)) , . . . , P _((trans)(m−2)) ,P _((trans)(m−1)) , P _((trans)m)}  (7)

{P _((page-hits)) }={ . . . , P _((page-hits)(m−n)) , . . . , P_((page-hits)(m−2)) , P _((page-hits)(m−1)) , P _((page-hits)m)}  (8)

{P _(visitors) }={ . . . , P _((visitors)(m−n)) , . . . , P_((visitors)(m−2)) , P _((visitors)(m−1)) , P _((visitors)m)}  (9)

{P _(hosts) }={ . . . , P _((hosts)(m−n)) , . . . , P _((hosts)(m−2)) ,P _((hosts)(m−1)) , P _((hosts)m})  (10)

{P _(delay) }={ . . . , P _((delay)(m−n)) , . . . , P _((delay)(m−2)) ,P _((delay)(m−1)) , P _((delay)m})  (11)

[0024] With respect to estimating revenue using the current Internetmetrics, the revenue model resulting from the regression analysis instep 104 is a quarterly model because the regression analysis isperformed on quarterly representations of the past data points. As such,if a full quarter of current metric data has been collected, this datacan be combined and scaled to a quarterly count to estimate the currentquarterly revenue. However, in accordance with the methods of ourinvention, the revenue model can also be scaled to daily, weekly, andmonthly revenue models and can be used to estimate revenue for thecurrent day, week, or month by applying corresponding expressions of thecurrent data. The use of the revenue model is further described below instep 106.

[0025] Turning to the second general step of our invention, step 104,the revenue data set and past Internet data sets obtained for a givencompany from the data collection step above are next statisticallyanalyzed to generate revenue models of this company. Specifically, steps104-A through 104-D illustrate the steps a computer, for examplecomputer 202 in FIG. 2, would perform to generate revenue models of agiven company and to select a given model to ultimately estimate currentrevenue and project future revenue. Methods in accordance with thepresent invention use regression analysis techniques to generate andselect this model.

[0026] As indicated above, depending on the type of Internet company,more than one type of Internet metric may apply. However, it is notreadily apparent which metric or whether a combination of metrics willprovide the “best” prediction of revenue. As such, under methodsconsistent with our invention, a plurality of revenue models usingdifferent combinations of the metric variables are first generated andfrom these models the model most likely to yield the best revenueestimate is determined based on statistical characteristics, such as thecoefficient of determination (“r²”). Specifically, revenue is firstmodeled with respect to each metric independently and then modeled withrespect to combinations of metrics, resulting in a plurality of revenuemodels. The model most likely to yield the “best” revenue estimate isthen determined and used to estimate current revenue and to projectfuture revenue.

[0027] For the purpose of discussion, the following discussion assumesthat transactions, page-hits, visitors, and hosts Internet metrics applyto a given company to be analyzed. However, as indicated above, only oneor two metrics may be applicable to a given company, in which case fewermodels are generated. In addition, the methods of our invention do notpreclude the use of additional metrics, as these metrics may evolve asthe Internet industry continues to mature. As such, additional modelsmay be generated.

[0028] Beginning with step 104-A, a plurality of revenue models is firstgenerated wherein each model uses either a single metric variable ormultiple metric variables, the latter models being generated todetermine if multiple metrics will have statistical characteristics thatwill most likely yield a better estimate of revenue than any one metrictaken individually. Starting with step 104-A1, the individual metricmodels are first generated, where each model has the form of the linearequation:

R=aM+b  (12)

[0029] where “R” is the estimated revenue, “M” is the quarterly Internetmetric, and “a” and “b” are unknown coefficients. While this model canchange in the future as the nature of the E-Commerce industry changes,the model in equation (12) has been shown to provide an accurate fitbetween revenue and the Internet metrics. The result of this first stepis four models of the form:

R _((trans))=(a _((trans)))(M _((trans)))+b _((trans))  (13)

R _((page-hits))=(a _((page-hits)))(M _((page-hits)))+b_((page-hits))  (14)

R _((visitors))=(a _((visitors)))(M _((visitors)))+b _((visitors))  (15)

R _((hosts))=(a _((hosts)))(M _((hosts)))+b _((host))  (16)

[0030] In steps 104-A2 and 104-A3, the “multiple” Internet metricrevenue models are generated. (Note, as indicated above, this discussionassumes that more than one Internet metric applies to a given Internetcompany. If only one metric applies, steps 104-A2 and 104-A3 are neverexecuted, step 104-A1 results in a single model, and this model issubsequently used in step 106 below to estimate and forecast revenue.)Our research has shown that the Internet metrics may have a collinearrelationship and as such, the variables must be “combined” to addressthis issue. We chose to combine the metrics using “standard sums”,whereby the Internet metrics are standardized using relative unitweights and then added to create a new set of Internet metrics. Each newmetric represents a unique combination of the original Internet metrics.Note that nothing in our invention precludes the use of other methods,such as principal component analysis, to combine two or more metrics.Using each new metric, revenue is again modeled multiple times whereineach model has the form of the linear equation:

R=aM′+b  (17)

[0031] where “R” is the estimated revenue, “M′” is the new Internetmetric, and “a” and “b” are unknown coefficients.

[0032] Beginning with step 104-A2, the new set of Internet metrics iscreated through the “standard sums” technique using combinations of twoor more of the quarterly representations of the existing Internetmetrics. The result is a new set of metrics, each with a correspondingset of past quarterly data points. Assuming the presence of four metricsas above, eleven new metrics are created as shown by Table 1, the firstcolumn showing the new Internet metrics and the second column showingthe constituent Internet metrics that comprise each new metric. TABLE 1Combined Internet Metrics New Internet Metric Component Metrics  1P_((trans,page-hits)) P_((trans)), P_((page-hits))  2P_((trans,visitors)) P_((trans)), P_((visitors))  3 P_((trans,hosts))P_((trans)), P_((hosts))  4 P_((page-hits,visitors)) P_((page-hits)),P_((visitors))  5 P_((page-hits,hosts)) P_((page-hits)), P_((hosts))  6P_((visitors,hosts)) P_((visitors)), P_((hosts))  7P_((trans,page-hits,visitors)) P_((trans)), P_((page-hits)),P_((visitors))  8 P_((trans,page-hits,hosts)) P_((trans)),P_((page-hits)), P_((hosts))  9 P_((trans,visitors,hosts)) P_((trans)),P_((visitors)), P_((hosts)) 10 P_((page-hits,visitors,hosts))P_((page-hits)), P_((visitors)), P_((hosts)) 11P_((trans,page-hits,hosts)) P_((trans)), P_((page-hits)),P_((visitors)), P_((hosts))

[0033] Specifically, the “combining” of the metrics by use of “standardsums” is performed by dividing each data point of the constituent pastquarterly Internet metric data sets by a weighting factor and then“summing” corresponding data points (actually, only the most recent “n”elements need be summed). The result is a new metric and correspondingset of “n” past quarterly data points. This procedure is shown below inequations (18), (24), and (28) for the “P_((trans,page-hits))”,“P_((trans,page-hits,visitors))”, and“P_((trans,page-hits,visitors,hosts))” metrics respectively. The othereight metrics are similarly defined by equations (19) to (23) and (25)to (27), not shown. $\begin{matrix}\begin{matrix}{\left\{ P_{({{trans},{{page} - {hits}}})} \right\} = \quad {\left\{ \frac{\left\{ P_{({trans})} \right\}}{W_{({trans})}} \right\} + \left\{ \frac{\left\{ P_{({{page} - {hits}})} \right\}}{W_{({trans})}} \right\}}} \\{= \quad \left\{ {\left( {\frac{P_{{({trans})}{({m - n})}}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}{({m - n})}}}{W_{({{page} - {hits}})}}} \right),\ldots \quad,\left( {\frac{P_{{({trans})}m}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}m}}{W_{({{page} - {hits}})}}} \right)} \right\}} \\{\quad \vdots}\end{matrix} & (18) \\\begin{matrix}{\left\{ P_{({{trans},{{page} - {hits}},{visitors}})} \right\} = \quad {\left\{ \frac{\left\{ P_{({trans})} \right\}}{W_{({trans})}} \right\} + \left\{ \frac{\left\{ P_{({{page} - {hits}})} \right\}}{W_{({{page} - {hits}})}} \right\} + \left\{ \frac{\left\{ P_{({visitors})} \right\}}{W_{({visitors})}} \right\}}} \\{= \quad \left\{ {\left( {\frac{P_{{({trans})}{({m - n})}}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}{({m - n})}}}{W_{({{page} - {hits}})}} + \frac{P_{{({visitors})}{({m - n})}}}{W_{({visitors})}}} \right),\ldots \quad,\left( {\frac{P_{{({trans})}m}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}m}}{W_{({{page} - {hits}})}} + \frac{P_{{({visitors})}m}}{W_{({visitors})}}} \right)} \right\}} \\{\quad \vdots}\end{matrix} & (24) \\\begin{matrix}{\left\{ P_{({{trans},{{page} - {hits}},{visitors},{hosts}})} \right\} = \quad {\left\{ \frac{\left\{ P_{({trans})} \right\}}{W_{({trans})}} \right\} + \left\{ \frac{\left\{ P_{({{page} - {hits}})} \right\}}{W_{({{page} - {hits}})}} \right\} + \left\{ {\frac{\left\{ P_{({visitors})} \right\}}{W_{({visitors})}} + \left\{ \frac{\left\{ P_{({hosts})} \right\}}{W_{({hosts})}} \right\}} \right\}}} \\{= \quad \begin{Bmatrix}{\left( {\frac{P_{{({trans})}{({m - n})}}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}{({m - n})}}}{W_{({{page} - {hits}})}} + \frac{P_{{({visitors})}{({m - n})}}}{W_{({visitors})}} + \frac{P_{{({hosts})}{({m - n})}}}{W_{({hosts})}}} \right),\ldots \quad,} \\\left. {\frac{P_{{({trans})}m}}{W_{({trans})}} + \frac{P_{{({{page} - {hits}})}m}}{W_{({{page} - {hits}})}} + \frac{P_{{({visitors})}m}}{W_{({visitors})}} + \frac{P_{{({hosts})}m}}{W_{({hosts})}}} \right)\end{Bmatrix}}\end{matrix} & (28)\end{matrix}$

[0034] where “W_((trans))”, “W_((visitors))”, and “W_((page-hits))” arethe weighting factors. Our invention currently defines the weightingfactor as the standard deviation of each Internet metric data set ,equations (7)-(10), over the “n” most recent values. Our research hasshown that the collinearity between the metrics is adequately accountedfor by using standard deviation as the weighting factor. However, ourinvention does not preclude the use of other weighting factors. The“W_((trans))”, “W_((visitors))” and “W_((page-hits))” “W_((hosts))”weighting factors are shown in equations (29)-(32) below.$\begin{matrix}{W_{({trans})} = {\sigma_{({trans})} = \frac{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({trans})}i} \right)^{2}} - {(n)\left( \overset{\_}{P_{({trans})}} \right)^{2}}}{\left( {n - 1} \right)}}} & (29) \\{W_{({{page} - {hits}})} = {\sigma_{({{page} - {hits}})} = \frac{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({{page} - {hits}})}i} \right)^{2}} - {(n)\left( \overset{\_}{P_{({{page} - {hits}})}} \right)^{2}}}{\left( {n - 1} \right)}}} & (30) \\{W_{({visitors})} = {\sigma_{({visitors})} = \frac{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({visitors})}i} \right)^{2}} - {(n)\left( \overset{\_}{P_{({visitors})}} \right)^{2}}}{\left( {n - 1} \right)}}} & (31) \\{W_{({hosts})} = {\sigma_{({hosts})} = \frac{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({hosts})}i} \right)^{2}} - {(n)\left( \overset{\_}{P_{({hosts})}} \right)^{2}}}{\left( {n - 1} \right)}}} & (32)\end{matrix}$

[0035] where “{overscore (P_((trans)))}”, {overscore (P_((page-hits)))},{overscore (P_((visitors)))}, and {overscore (P_((hosts)))} are theaverage transactions, page-hits, visitors, and hosts metric values ascomputed over the “n” most recent data elements in equations (7)-(10),respectively.

[0036] In step 104-A3, the eleven new metrics are each modeled as alinear equation resulting in eleven additional models, three of which,“R_((trans,page-hits))”, “R_((trans,page-hits,visitors))”, and“R_((trans,page-hits,visitors, hosts))” are shown below in equations(33), (39), and (43). The remaining eight equations are similarlydefined by equations (34) to (38) and (40) to (42), not shown.

R _((trans,page-hits))=(a _((trans,page-hits)))(M_((trans,page-hits)))+b _((trans,page-hits))  (33)

R _((trans,page-hits, visitors))=(a _((trans,page-hits, visitors)))(M_((trans,page-hits, visitors)))+b _((trans,page-hits, visitors))  (39)

R _((trans,page-hits, visitors,hosts))=(a_((trans,page-hits, visitors,hosts)))(M_((trans,page-hits, visitors,hosts)))+b_((trans,page-hits, visitors,hosts))  (43)

[0037] In step 104-B, the “least squares line” or “regression line” isdetermined for each individual and multiple metric model, (13)-(16) and(33)-(43), by determining the least squares estimate for each of themodel coefficients: “a_((trans))”, “b_((trans))”,“a_((trans,page-hits))”,“b_((trans,page-hits))”,“a_((trans,page-hits, visitors))”, etc. Usingthe revenue data set equation (1), the past individual metric data setsequations (7)-(10), and the new combined metric data sets equations(18)-(28), the least squares estimate of each coefficient is determined,as shown in equations (44)-(75) for the “a_((trans))”, “b_((trans))”,“a_((trans, page-hits))”, “b_((trans, page-hits))”,“a_((trans, page-hits, visitors, hosts))”,“b_((trans, page-hits, visitors, hosts))”coefficients. The least squares estimate equations for the remainingtwenty-four coefficients are similarly defined by equations (46) to (51)and (54) to (73), not shown $\begin{matrix}{{\hat{a}}_{({trans})} = \frac{{\sum\limits_{i = {m - n}}^{m}{\left( P_{{({trans})}i} \right)\left( R_{i} \right)}} - {(n)\left( \overset{\_}{P_{trans}} \right)\left( \overset{\_}{R} \right)}}{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({trans})}i} \right)^{2}} - {(n)\left( \overset{\_}{P_{({trans})}} \right)^{2}}}} & (44)\end{matrix}$

{circumflex over (b)} _((trans))=({overscore (R)})−(â_((trans)))({overscore (P _((trans)))})  (45)

[0038] ⋮ $\begin{matrix}{{\hat{a}}_{({{trans},{{page} - {hits}}})} = \frac{{\sum\limits_{i = {m - n}}^{m}{\left( P_{{({{trans},{{page} - {hits}}})}i} \right)\left( R_{i} \right)}} - {(n)\left( \overset{\_}{P_{({{trans},{{page} - {hits}}})}} \right)\left( \overset{\_}{R} \right)}}{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({{trans},{{page} - {hits}}})}i} \right)^{2}} - {(n)\left( P_{({{trans},{{page} - {hits}}})} \right)^{2}}}} & (52)\end{matrix}$

{circumflex over (b)} _((trans, page-hits))=({overscore (R)})−(â_((trans, page-hits)))({overscore (P _((trans, page-hits)))})  (53)

[0039] $\begin{matrix}{\vdots {{\hat{a}}_{({{trans},{{page} - {hits}},{visitors},{hosts}})} = \frac{\begin{matrix}{{\sum\limits_{i = {m - n}}^{m}{\left( P_{{({{trans},{{page} - {hits}},{visitors},{hosts}})}i} \right)\left( R_{i} \right)}} -} \\{(n)\left( \overset{\_}{P_{({{trans},{{page} - {hits}},{visitors},{hosts}})}} \right)\left( \overset{\_}{R} \right)}\end{matrix}}{\begin{matrix}{{\sum\limits_{i = {m - n}}^{m}\left( P_{{({{trans},{{page} - {hits}},{visitors},{hosts}})}i} \right)^{2}} -} \\{(n)\left( \overset{\_}{P_{({{trans},{{page} - {hits}},{visitors},{hosts}})}} \right)^{2}}\end{matrix}}}} & (74)\end{matrix}$

{circumflex over (b)} _((trans, page-hits, visitors, hosts))=({overscore(R)})−(â _((trans, page-hits, visitors, hosts)))({overscore (P_((trans, page-hits, visitors, hosts)))})  (75)

[0040] where “n” is the number of past data points in the revenue andmetric data sets deemed to be predictive of the current revenue (asindicated above, n=6 quarters is currently used),“P_((trans)),”“P_((trans, page-hits))”,“P_((trans, page-hits, visitors, hosts))”, etc. are data points from theoriginal and new metric data sets equations (7)-(10) and (18)-(28),“{overscore (R)}” is data points from the revenue data set equation (1),“{overscore (P_((trans)))}”, “{overscore (P_((trans, page-hits)))}”,“{overscore (P_((trans, page-hits, visitors, hosts)))}”, etc. are the“average-metric-value” of each original/new metric data set as computedover the “n” most recent data elements in equations (7)-(10) and(18)-(28), and “{overscore (R)}” is the “average revenue value” ascomputed over the “n” most recent data elements in equation (1). Theresult of step 104-B is fifteen revenue estimation equations as shown inequations (76)-(90) ((81) to (85) and (87) to (89) not being shown) andrepresented by Models 150 in FIG. 1.

{circumflex over (R)} _((trans))=(â _((trans)))(M_((trans)))+{circumflex over (b)} _((trans))  (76)

{circumflex over (R)} _((page-hits))=(â _((page-hits)))(M_((page-hits)))+{circumflex over (b)} _((page-hits))  (77)

{circumflex over (R)} _((vistors))=(â _((visitors)))(M_((visitors)))+{circumflex over (b)} _((visitors))  (78)

{circumflex over (R)} _((hosts))=(â _((hosts)))(M_((hosts)))+{circumflex over (b)} _((hosts))  (79)

{circumflex over (R)} _((trans, page-hits))=(â _((trans, page-hits)))(M_((trans, page-hits)))+{circumflex over (b)} _((trans, page-hits))  (80)

{circumflex over (R)} _((trans, page-hits, visitors))=(â_((trans, page-hits, visitors)))(M_((trans, page-hits, visitors)))+{circumflex over (b)}_((trans.page-hits, visitors))  (86)

{circumflex over (R)} _((trans, page-hits, visitors, hosts))=(â_((trans, page-hits, visitors, hosts)))(M_((trans, page-hits, visitors, hosts)))+{circumflex over (b)}_((trans.page-hits, visitors, hosts))  (90)

[0041] Each of these equations can be used to estimate the currentquarter's revenue if quarterly representations of the combined metricsare available (i.e., a full quarter of data points have been collected).

[0042] As indicated, the completion of step 104-B results in a pluralityof individual metric and multiple metric revenue models, as shown by theequations above. The next step is to determine which of these models hasthe statistical properties to likely be the “best” estimator of currentrevenue. Different methods exist in the art for determining how well a“least squares equation” performs. One method used by our invention isto compute the “coefficient of determination”, also called “r²”, for theequation, although nothing precludes the use of other methods. Thecoefficient of determination for any least squares equation ranges invalue between “0” and “1” with “0” indicating a weak model fit and “1”indicating a strong model fit.

[0043] Beginning with step 104-C, the coefficient of determination iscomputed for each of the determined metric models, equations (76)-(90).The model with the largest resultant “value” is then chosen, in step104-D, as the model to estimate current revenue. The equations tocompute the coefficient of determination for “{circumflex over(R)}_((trans))”, “{circumflex over (R)}_((trans, page-hits))”,“{circumflex over (R)}_((trans, page-hits, visitors))”, and “{circumflexover (R)}_((trans, page-hits, visitors, hosts))” are shown below inequations (91), (95), (101), and (105). The remaining eleven equations,(92) to (94), (96) to (100), and (102) to (104), are similarly defined.$\begin{matrix}{{r_{({trans})}^{2} = {1 - \frac{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - {\hat{R}}_{{({trans})}i}} \right)^{2}}{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - \overset{\_}{R}} \right)^{2}}}}\vdots} & (91) \\{{r_{({{trans},{{page} - {hits}}})}^{2} = {1 - \frac{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - {\hat{R}}_{{({{trans},{{page} - {hits}}})}i}} \right)^{2}}{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - \overset{\_}{R}} \right)^{2}}}}\vdots} & (95) \\{r_{({{trans},{{page} - {hits}},{visitors}})}^{2} = {1 - \frac{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - {\hat{R}}_{{({{trans},{{page} - {hits}},{visitors}})}i}} \right)^{2}}{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - \overset{\_}{R}} \right)^{2}}}} & (101) \\{r_{({{trans},{{page} - {hits}},{visitors},{hosts}})}^{2} = {1 - \frac{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - {\hat{R}}_{{({{trans},{{page} - {hits}},{visitors},{hosts}})}i}} \right)^{2}}{\sum\limits_{i = {m - n}}^{m}\left( {R_{i} - \overset{\_}{R}} \right)^{2}}}} & (105)\end{matrix}$

[0044] where “{circumflex over (R)}_((trans)),” is the estimated revenueusing the “n” most recent data points from the transactions metric dataset equation (7), “{circumflex over (R)}_((trans, page-hits))” is theestimated revenue using the “n” most recent data points from thecombined transaction/page-hits metric data set equation (18), etc.

[0045] In step 104-D, the model with largest coefficient ofdetermination is chosen as the model that will most likely provide thebest estimate of current revenue. This model, for discussion purposes,will be referred to as:

{circumflex over (R)}=âM′+{circumflex over (b)}  (106)

[0046] where “â” and “{circumflex over (b)}” are the “a” and “b” leastsquares estimate coefficients from the chosen model, and “M′” is themetric (either single or multiple) of the chosen model. In anotherembodiment of our invention, the individual or multiple metric used tomake prior revenue estimates is also considered in step 104-D whenchoosing the present model.

[0047] Turning to the third general step of our invention, step 106,equation (106) can now be used to estimate current revenue and tostatistically project future revenue of the modeled Internet company.Revenue estimation will first be described followed by revenueprojection.

[0048] Equation (106) can be used to estimate a company's currentrevenue over a given period of time based on current measurements of theM′ metric. M′ is either an individual or multiple metric. Assume firstthat M′ is an individual metric. As indicated above, the collectionmethods currently used by our invention measure the metrics on a weeklybasis, although daily, monthly, and quarterly measurements can also bemade. Assuming weekly measurements are made, the (k+1)^(th), (k+2)^(th),etc. data points from the metric data sets, equations (2)-(6), can nowbe used to estimate revenue. Specifically, if a full quarter of weeklymeasurements have been made (e.g., thirteen measurements), the resultantdata points can be combined and scaled to a quarterly count andsubstituted for M′ in equation (106) to estimate revenue for the currentquarter. However, a more useful application of our invention is toestimate revenue as soon as possible. As such, under methods consistentwith our invention, equation (106) can be scaled to estimate revenue forthe current week and month, as shown by equations (107) and (108),respectively, were “x_(week)” is the number of weeks in the quarter and“x_(month)” is the number of months in the quarter. $\begin{matrix}{{\hat{R}}_{week} = {{\frac{\hat{a}}{x_{week}}M^{\prime}} + \frac{\hat{b}}{x_{week}}}} & (107) \\{{\hat{R}}_{month} = {{\frac{\hat{a}}{x_{month}}M^{\prime}} + {\frac{\hat{b}}{x_{month}}.}}} & (108)\end{matrix}$

[0049] Hence, using equation (107), the weekly metric data points can beused to estimate revenue on a week-by-week basis. By combining andscaling the weekly data points to monthly counts, equation (108) can beused to estimate revenue on a monthly basis. Similarly, if the metric ismeasured on a daily basis, equation (106) can be scaled to estimatedaily revenue.

[0050] Assume next that M′ is a multiple metric and, for discussionpurposes, is a combination of the “transactions” and “page-hits”metrics. Similar to the individual metric, the combined metric can beused to estimate revenue for the current week, month, and quarterthrough equations (106), (107), and (108). However, similar to step104-A2 above, the (k+1)^(th), (k+2)^(th), etc. data points of the“{D_(trans)}” and “{D_(page-hits)}” data sets, equations (2) and (3),cannot be applied to the revenue equations until these data points arecombined using principals similar to equation (18) (i.e., standardsums).

[0051] As such, under methods consistent with our invention, the“{D_(trans)}” and “{D_(page-hits)}” data sets are first individuallyexpressed as quarterly data points, monthly data points, or maintainedas weekly data points, depending on the desired estimate, using all datapoints from the (k−j)^(th) through the current measurement. Next, usingthe “standard sum” principals set forth in step 104-A2, a weightingfactor is determined for each of the resultant data sets using thestandard deviation of these data sets. Finally, the data points of theresultant data sets are weighted and corresponding points are summed,resulting in a new combined data set that can be applied to equations(106), (107), and (108) to estimate current revenue.

[0052] For example, using equation (107) to estimate revenue for thecurrent week, equations (109) and (110) show the weekly weightingfactors for the “{D_(trans)}” and “{D_(page-hits)}” data sets$\begin{matrix}{W_{({trans})} = {\sigma_{({trans})} = \frac{{\sum\limits_{i = {k - j}}^{k + g}\left( D_{{({trans})}i} \right)^{2}} - {\left( {j + g} \right)\left( \overset{\_}{D_{({trans})}} \right)^{2}}}{\left( {j + g - 1} \right)}}} & (109) \\{W_{({{page} - {hits}})} = {\sigma_{({{page} - {hits}})} = \frac{{\sum\limits_{i = {k - j}}^{k + g}\left( D_{{({{page} - {hits}})}i} \right)^{2}} - {\left( {j + g} \right)\left( \overset{\_}{D_{({{page} - {hits}})}} \right)^{2}}}{\left( {j + g - 1} \right)}}} & (110)\end{matrix}$

[0053] where (k+g) is the most current weekly data point, and“{overscore (D_((trans)))}” and “{overscore (D_((page-hits)))}” areaverage weekly metric values over the (k−j)^(th) to (k+g)^(th) dataelements. Using these weighting factors, the resultant combined data setis shown in equation (111), the data points of which can be used withequation (107) to estimate the revenue for each week. $\begin{matrix}\begin{matrix}{\left\{ C_{({{trans},{{page} - {hits}}})} \right\} = \quad {\left\{ \frac{\left\{ D_{({trans})} \right\}}{W_{({trans})}} \right\} + \left\{ \frac{\left\{ D_{({{page} - {hits}})} \right\}}{W_{({trans})}} \right\}}} \\{= \quad \left\{ {\left( {\frac{D_{{({trans})}{({k - j})}}}{W_{({trans})}} + \frac{D_{{({{page} - {hits}})}{({k - j})}}}{W_{({{page} - {hits}})}}} \right),\ldots \quad,} \right.} \\{\quad \left. \left( {\frac{D_{{({trans})}{({k + g})}}}{W_{({trans})}} + \frac{D_{{({{page} - {hits}})}{({k + g})}}}{W_{({{page} - {hits}})}}} \right) \right\}}\end{matrix} & (111)\end{matrix}$

[0054] In accordance with the methods of our invention, FIG. 3 showsweekly revenue estimates and actual quarterly revenue results for fourquarters for an Internet company. The thirteen points comprisingquarters 302, 304, 306, and 308 represent weekly revenue estimates usingthe methods of our invention. Once a full quarter of revenue estimatesare made, the resultant values can be summed to estimate the quarterlyrevenue, prior to the actual revenue being reported. (As reference, bars320, 322, 324, and 326 represent actual reported quarterly revenue.)

[0055] In addition to estimating revenue for the current day, week, ormonth, it is also useful, in advance of the availability of metric datafor future time periods, to project revenue for the remaining days,weeks, or months of the quarter and to subsequently use the estimatedand projected values to project the quarterly revenue as a whole. Forexample, the five points comprising quarter 310 represent weekly revenueestimates for the current quarter. These points can be used tostatistically project the revenue for each of the remaining eight weekscomprising the quarter. Subsequently, the five revenue estimates and theeight revenue projections can be summed to project the quarterlyrevenue, as shown by bar 328. Under methods consistent with ourinvention, a “running average technique” is used to make these forecaststhereby capturing the trend of the revenue estimates, however nothingprecludes the use of other projection techniques.

[0056] More explicitly, the running average technique is used tostatistically project the revenue for the remaining eight weeks ofquarter 310 as follows. The sixth weekly revenue point is projected byaveraging the prior N weekly revenue points beginning with the fifthweek. The seventh weekly revenue point is projected by averaging theprior N weekly revenue points beginning with the sixth week (i.e., theprojected sixth week is used to project the seventh week). This methodis continued until the thirteenth weekly revenue point is projected byaveraging the prior N weekly revenue points beginning with the twelfthweek. The quarterly revenue is then projected by summing all thirteenweeks, this projection being represented by bar 328 in FIG. 3. Thisembodiment of our invention currently uses N=6 although other values canbe used.

[0057] The above-described embodiment of our invention is intended to beillustrative only. Numerous other embodiments may be devised by thoseskilled in the art without departing from the spirit and scope of ourinvention.

We claim:
 1. A method for estimating current revenue of a company, saidmethod comprising the steps performed by a computer of: obtaining thecompany's actual quarterly revenue performance for a plurality of priorquarters, obtaining data points for an Internet metric over theplurality of prior quarters, wherein the Internet metric is related tothe company, generating a revenue model for the company using theobtained actual quarterly revenue performance and the obtained Internetmetric data points, obtaining one or more current data points for theInternet metric, and estimating the company's current revenue byapplying the obtained current Internet metric data points to thegenerated revenue model.
 2. The method of claim 1 wherein the obtainedactual quarterly revenue performance and the obtained Internet metricdata points for the plurality of past quarters are obtained for no morethan six prior quarters.
 3. The method of claim 1 wherein the generatedrevenue model is a quarterly revenue model.
 4. The method of claim 3further comprising the step of scaling the generated quarterly revenuemodel to a weekly revenue model, and wherein the obtained currentInternet metric data points comprise one week of data points, andwherein the estimated company's current revenue is revenue for this oneweek.
 5. The method of claim 1 wherein the generated revenue model is aweekly revenue model, the obtained current Internet metric data pointscomprise one week of data points, and wherein the estimated company'scurrent revenue is revenue for this one week, said method furthercomprising the steps of: obtaining current data points for the Internetmetric over a plurality of weeks, estimating the company's weeklyrevenue for each of the plurality of weeks, and summing the plurality ofestimated weekly revenues to obtain the company's revenue for thecurrent quarter.
 6. The method of claim 1 wherein the generated revenuemodel is a weekly revenue model and wherein the estimated company'scurrent revenue is for the current week, said method further comprisingthe step of projecting revenue for a following week.
 7. The method ofclaim 6 wherein the revenue projection is made through a running averagetechnique.
 8. The method of claim 7 further comprising the steps of:projecting revenue for all remaining weeks in a quarter beyond thecurrent week, and summing all revenue estimates and revenue projectionsfor a quarter to obtain a quarterly revenue estimate.
 9. The method ofclaim 1 wherein the Internet metric is a page-hits metric, a visitorsmetric, a transactions metric, or a hosts metric.
 10. A method forestimating current revenue of a company, said method comprising thesteps performed by a computer of: obtaining data points for at least twoInternet metrics over a plurality of past quarters, wherein said atleast two Internet metrics are related to the company, creating newInternet metric data points over the plurality of past quarters bycombining the data points from the at least two Internet metrics,generating a revenue model for the company by using the created newInternet metric data points, creating one or more new Internet metricdata points for the current quarter by combining one or more currentdata points obtained for each of the at least two Internet metrics, andestimating the company's current revenue by applying the created newInternet metric data points to the generated revenue model.
 11. Themethod of claim 10 wherein the new Internet metric data points for thepast and current quarters are created through standard sums.
 12. Themethod of claim 11 wherein standard deviations are used as a weightingfactor in the standard sums.
 13. The method of claim 10 wherein thegenerated revenue model is a quarterly revenue model, the method furthercomprising the step of scaling the generated quarterly revenue model toa weekly revenue model, and wherein the created new Internet metric datapoints comprise one week of data points, and wherein the estimatedcompany's current revenue is revenue for this one week.
 14. A method forestimating current revenue of a company, said method comprising thesteps performed by a computer of: obtaining data points for each of aplurality of Internet metrics over a plurality of past quarters, whereinsaid Internet metrics are related to the company, generating a pluralityof revenue models for the company by using the obtained data points foreach of the plurality of Internet metrics, choosing from the pluralityof revenue models a revenue model for estimating current revenue, andestimating the company's current revenue by applying currently obtainedInternet metric data points to the chosen revenue model.
 15. The methodof claim 14 wherein each of the plurality of generated revenue modelscorresponds to one of the plurality of Internet metrics.
 16. The methodof claim 15 further comprising the step of: combining the past quarterof data points for each of the plurality of Internet metrics to createone or more new Internet metrics and corresponding data points, andwherein the plurality of generated revenue models further includes arevenue model corresponding to each of the new Internet metrics.
 17. Themethod of claim 16 wherein the choosing step comprises the steps of:computing r² for each of the plurality of generated models, and choosingthe revenue model for estimating current revenue based on the largest r²value, where r² is the coefficient of determination.
 18. The method ofclaim 17 wherein the choosing step further comprises selecting theInternet metric or combined Internet metric used to make a prior revenueestimation.
 19. The method of claim 14 wherein each of the plurality ofInternet metrics is a page-hits metric, a visitors metric, atransactions metric, or a hosts metric.